Des troubles moteurs ont été décrits dans les Troubles du Spectre Autistiques (TSA), toutefois tous les enfants atteints de TSA ne montrent pas de diminution des performances motrices. La nature et l'origine des perturbations motrices dans les TSA ne sont pas claires. Les processus neuro-développementaux, en lien avec la maturation du système nerveux central, sont peu explorés dans les TSA, or ces processus sous-tendent les performances motrices. Peu d'études portent sur l'analyse fine de la sémiologie des fonctions neuro-psychomotrices dans les TSA et l'existence d'une trajectoire neuro-développementale de ces fonctions n'est pas connue chez les enfants avec TSA. L'objectif de cette étude est de mettre en évidence la sémiologie des troubles psychomoteurs auprès d'enfants avec TSA, à l'aide d'une batterie standardisée Française d'évaluation développementale des fonctions neuro-psychomotrices de l'enfant (NP-MOT) (Vaivre-Douret, 2006). L'évaluation neuro-psychomotrice complète les évaluations de premières instances (psychiatrique; psychologique; compréhension; psychomotrice). L'identification d'un profil clinique neuro-psychomoteur, l'identification de troubles ou décalages par rapport à une norme de référence, la mise en évidence de fonctions cérébrales éventuellement touchées dans les TSA devraient permettre de mieux comprendre l'origine et la nature des troubles observés dans les TSA. Les résultats de plus en plus nombreux concernant la motricité chez ces enfants doivent pouvoir également être analysés au regard des évaluations cognitives et neuro-cognitives, afin d'affiner le profil de développement et permettre ainsi de mieux comprendre la nature des troubles autistiques parmi une comorbidité d'éventuels autres dysfonctionnements. / Motor disorders have been described in the Autistic Spectrum Disorders (ASD), however all children with ASD show no decrease in motor performances. The nature and origin of motor disturbances in ASD are unclear. Neurodevelopmental processes linked to the maturation of the central nervous system, are not really explored in ASD, but these processes underlie motor performances. Few studies trat of an acute semiology of motor abnormalities in ASD and the existence of a neuro-developmental trajectory of neuro-psychomotor functions is not known in children with ASD. The aim of this study is to highlight the semiology of psychomotor disorders among children with ASD, using a French standardized neurodevelopmental assessment tool (NP-MOT) (Vaivre-Douret, 2006). Evaluations of the first instances (psychiatric; psychological; understanding; psychomotor) were supplemented by a standardized assessment battery of neuro-developmental psychomotor functions (NP-MOT). The identification of a neuro-psychomotor clinical profile, identification of problems or discrepancies compared to a standard reference, the identification of potentially affected brain functions in ASD should provide a better understanding of the origin and nature the observed disorders in ASD. The results, more and more numerous concerning motor skills in these children, should be able to be analyzed in light of cognitive or neuro-cognitive assessments and should allow to refine the profile of development and thereby enable a better understanding of the nature of autism among a comorbidity other possible malfunctions.

Este projeto consiste em um sistema de navegação autônomo baseado em redes neurais nebulosas modulares capacitando o robô a alcançar alvos, ou pontos metas, em ambientes desconhecidos. Inicialmente, o sistema não tem habilidade para a navegação, após uma fase de experimentos com algumas colisões, o mecanismo de navegação aprimora-se guiando o robô ao alvo de forma eficiente. Uma arquitetura híbrida inteligente é apresentada para este sistema de navegação, baseada em redes neurais artificiais e lógica nebulosa. A arquitetura é hierárquica e costitiui-se de dois módulos responsáveis por gerar comportamentos inatos de desvio de obstáculos e de busca ao alvo. Um mecanismo de aprendizagem por reforço, baseada em uma extensão da lei de Hebb, pondera os comportamentos inatos conflitantes ajustando os pesos sinápticos das redes neurais nos instantes de captura do alvo e de colisão contra obstáculos. A abordagem consolidada em simulação é validada em ambientes reais neste trabalho. Para tanto, este sistema foi implementado e testado no simulador Saphira, ambiente de simulação que acompanha o robô Pioneer I e que denota um estágio anterior aos testes em ambientes reais por apresentar comportamentos do robô similares aos comportamentos do robô móvel. Modificações na arquitetura híbrida foram necessárias para adaptar o sistema de navegação simulado ao sistema incorporado no Pioneer I. Experimentos em ambientes reais demonstraram a eficiência e a capacidade de aprendizagem do sistema de navegação, validando a arquitetura híbrida inteligente para aplicação em robôs móveis / This project consists in a autonomous navigation system based on modular neuro-fuzzy networks that is able to guide the robot in unknown environments from a initial point to the goal. Initially, the system is not able to navigate, but after a trial and error period and some collisions, it improves in guiding the robot to the goal efficiently. A intelligent hybrid architecture is presented for this naviga tion system based on artificial neural networks and fuzzy logic. This architecture is hierarquical and consists in two modules that generate innate behaviors, like obstacles avoiding and target reaching. A reinforcement learning mecanism, based on the extended Hebb law, balances this conflicting innate behaviors adjusting the neural network synaptic weights as obstacle and collision avoidance and target reaching takes place. In this project, the approach is consolidated in simulation and validated in real environments. To this end, this system has been implemented by using Saphira simulator and Pioneer I simulation environment. This simulated evironment is a previous stage of tests performed real time and presents simulated robot behaviors similar to real mobile robot behaviors. The hybrid architecture was modified to adapt the simulated navigation system into Pioneer I software. Experiments in a real environments show the efficiency and learning capabilities of the navigation system, validating the intelligent hybrid architecture for mobile robots applications

Les réseaux de neurones artificiels, dont le concept s'inspire du fonctionnement des cerveaux biologiques et de leurs capacités d'apprentissage, sont une approche prometteuse pour répondre aux nouveaux usages informatiques dits « cognitifs », tels que la reconnaissance d'images ou l'interaction en langage naturel. Néanmoins, leur mise en œuvre par des ordinateurs conventionnels est peu efficace. Une solution à ce problème est le développement de puces d'accélération matérielle spécialisées qui comportent :- des neurones, unités de traitement de l'information, pour lesquelles des circuits électroniques efficaces existent ;- des synapses, reliant les neurones mais aussi support matériel de l'apprentissage, par le biais de la modulation de leur conductance électrique (qualifiée de « plasticité synaptique »). Réaliser des synapses artificielles intégrables densément et capables d'apprendre in situ reste aujourd'hui un défi majeur.Ces travaux de thèse portent sur l'utilisation synaptique de nanocomposants mémoires innovants, dont certains comportements plastiques riches et intrinsèques sont analogues aux fonctionnalités que nous recherchons.Nous nous intéressons tout d'abord aux jonctions tunnel magnétiques à transfert de spin, développées dans l'industrie pour concevoir de nouvelles mémoires informatiques non volatiles. Nous montrons qu'il est aussi possible d'en faire des synapses artificielles binaires. Après la modélisation analytique de leur comportement naturellement stochastique, nous présentons comment exploiter ce dernier pour faciliter la mise en œuvre in situ d'une règle d'apprentissage probabiliste. À l'aide d'outils de simulation développés au laboratoire, nous étudions l'influence du régime de programmation sur la robustesse d'un système à la variabilité de telles synapses et sur leur consommation énergétique.Nous nous tournons ensuite vers des cellules électrochimiques métalliques Ag2S, d'autres nanocomposants mémoires innovants fabriqués et étudiés par des collaborateurs de l'Université de Lille I, qui y ont déjà observé plusieurs comportements plastiques. Nous avons découvert une plasticité supplémentaire, proche d'un comportement observé en neurosciences. Grâce à un modèle analytique simple permettant de comprendre les relations entre les différentes plasticités, nous montrons en simulation une preuve de concept d'apprentissage non supervisé qui repose sur l'interaction de ces multiples comportements.Pour finir, nous soulevons des pistes de réflexion sur les défis posés par les circuits nécessaires au bon fonctionnement d'un système utilisant comme synapses artificielles les nanocomposants étudiés, notamment lors de la lecture ou de l'écriture de ces derniers.Les résultats de cette thèse ouvrent la voie à la conception de systèmes neuro-inspirés capables d'apprendre en s'appuyant sur la richesse de comportements plastiques offerte par les nanocomposants mémoires innovants. / Artificial neural networks, which take some inspiration from the behavior of biological brains and their learning capabilities, are promising tools to address emerging computing uses known as “cognitive” tasks like classifying images or natural language interaction. However, implementing them on conventional computers is poorly efficient. A solution to this problem is to develop specialized acceleration chips which feature:• neurons, the information processing units, which can be implemented efficienctly with current electronic technologies;• synapses, the connections between the neurons which also support the learning process by adjusting their electrical conductance (“synaptic plasticity”). Implementing artificial synapses with high integration and on-line learning capabilities is still a challenge.This thesis explores the use of innovative memory nanodevices as artificial synapses: some of their rich plastic behaviors naturally implement features that are difficult to access with other devices.First, we investigate spin-transfer torque magnetic tunnel junctions, that are currently develop in industry as a new non volatile memory technology. We show that they can also be used as binary artificial synapses. After modeling their intrinsic stochastic behavior analytically, we describe how to harness this behavior to facilitate the implementation of an on-line probabilistic learning rule. With simulations tools developped in the laboratory, we detail the impact of the programming regime on the resilience of a system that uses such synapses, as well as on the system's power consumptionWe then investigate Ag2S electrochemical metalization cells, another type of innovative memory nanodevices fabricated and characterized by collaborators from Université de Lille I, who had already observed the existence of several plastic behaviors. We discovered an additional plasticity, close to a behavior known in neurosciences. With a simple analytical model that allows a better understanding of the relationships between theses plasticities, we show by simulations means a proof of concept of an unsupervised learning that relies on the interaction of the plastic behaviors theses nanodevices feature.Finally, we consider the challenges arising from the circuits that are required to read and write such artificial synapses in a neuro-inspired system.The results of this Ph.D. work pave the way for the design of neuro-inspired systems that can learn by harnessing the rich plastic behaviors that are featured by innovative memory nanodevices.

En associant les récentes techniques de neuro-imagerie (IRMf et Potentiels Evoqués) à la finesse des analyses syntaxiques des approches typologiques et formelles, cette recherche pluridisciplinaire se penche sur la question de la représentation des structures hiérarchiques qui caractérisent l’unité-phrase à travers les langues. La façon dont le cerveau humain représente, construit et l'esprit comprend les diverses structures de phrase, est en effet une des plus importantes questions qui restent encore largement irrésolues dans l’organisation cérébrale du langage. En nous appuyant sur la diversité des langues dans leur organisation syntaxique de l’unité-phrase, nous avons pu isoler différentes dimensions de cette complexité grâce aux propriétés syntaxiques du français dans la formation des questions, ainsi qu'aux spécificités des articulations Topique-Commentaire en chinois mandarin. Suite à une étude du marquage intonationel de la hiérarchie entre Topique et Commentaire, nous avons pu enregistrer les réponses cérébrales (PE) à ce type de constructions en contexte, et ainsi découvrir l’influence de sa signature prosodique sur son traitement en temps réel. Nos deux études d’IRMf apportent quand à elles un éclairage sur les bases neurales de deux dimensions de la complexité syntaxique de la phrase : sa structure hiérarchique et les transformations structurelles dont elle témoigne en cas de dislocation de ses éléments. La première étude, sur les interrogatives en français, met en lumière les corrélats cérébraux de différents types de movements syntaxiques, la seconde, sur les différents phénomènes topicaux du chinois, révèle les représentations et processus qui sont liés à l’activation par le Topique de l’interface entre l’unité-phrase et le niveau du discours. / Combining fine-grained linguistic analyses — from both typological and formal approaches to syntax — with neuro-imaging techniques (fMRI and ERP), this pluri-disciplinary research aims at investigating experimentally the issue of the hierarchical nature and complexity of the linguistic representation of sentence structure and its processing strategies across languages, specifically focusing on the case of Chinese Topic-Comment articulations and on French Interrogative constructions. The question of how the brain achieves sentence structure representation, building and understanding is often seen as one of the most important and unsolved issues of the neural organization of language. Leveraging on cross-linguistic invariance and variability in sentence hierarchical structure organization and building, we found in Chinese and French two exceptional testing grounds to isolate different syntactic complexity dimensions of the sentence-unit encoding. While the on-line auditory comprehension of sentence hierarchical structure in case of minimal intonational cues is investigated thanks to ERP recordings of Topic-Comment articulations in Chinese, two fMRI studies isolate two different syntactic complexity dimensions, respectively reflecting the sentence’s hierarchy and syntactic transformations. The first study, on French interrogative, seeks to isolate the neural correlates of different syntactic movement types. The second study, on Chinese sentence-discourse interface and Topics types, enables us to distinguish word-order surface complexity factors from syntactic movement transformations.

The aim of the dissertation entitled Neuro-Developmental Stimulation in special education teachers work is to explain a lesser-known phenomenon (persistent primary reflexes and sensory-sensitive integration disorders in children) to determine the prevalence of persistent primary reflexes in pupils from 5 to 8 years and verify effectiveness of the method Neuro- Developmental Stimulation as a possible intervention program for special educators. The theoretical basis is current knowledge about psychomotorics, primary reflexes, sensory perception and sensory-sensitive integration. The main part of the work is research into the prevalence of persistent primary reflexes. The research group consists 345 pupils from 5 to 8 years of age attending regular kindergartens and primary schools and 26 pupils aged 8 to 11 years attending a primary school established pursuant to Section 16, Paragraph 9 of the Czech Education law. Intervention by the Neuro-Developmental Stimulation method is verified in a case study. The next part of the research maps the experience of special pedagogue teachers and speech therapists with the method Neuro-Developmental Stimulation as an intervention program. The research has a quantitative approach. For data collection, questionnaires were distributed among all special pedagogues and...

Esta tesis propone una solución a la problemática actual del crecimiento poblacional de los adultos mayores a nivel mundial a través de un centro de atención residencial en el distrito de San Martín de Porres. Se analizan los antecedentes de la tipología, así como referentes a tomar en cuenta para tener una investigación teórica y arquitectónica completa que nos den una visión global del proyecto. A su vez, se analizan los usuarios para la elaboración de un programa arquitectónico que cumpla con satisfacer las distintas necesidades. Se consideran los aspectos físicos y psicológicos del adulto mayor para el planteamiento de la arquitectura, eligiendo como énfasis la neuro-arquitectura, la cual evitará el deterioro cognitivo y el aislamiento de los usuarios residentes. Por consiguiente, se analizarán referentes para el énfasis, el cual nos ayudará a obtener las características que debe tener la arquitectura dentro de los distintos espacios. Finalmente, mediante la sinergia del análisis del entorno urbano, como la teoría, el programa elaborado, y el énfasis se busca brindar un espacio residencial que cuente con un área de atención médica básica el cual fomente la participación activa del adulto dentro de un entorno urbano a nivel barrial, mejorando su calidad de vida. / This thesis proposes a solution to the worldwide current problem of the elderly growing population through a residential care center in San Martín de Porres. The antecedents of the typology are analyzed, as well as references to take into to have a complete theoretical and architectural investigation that gives us a global vision of the project. At the same time, the users are analyzed to create an architectural program that meets the different needs. The physical and psychological aspects of the elderly are considered for the approach to architecture, choosing neuro-architecture as emphasis in this project, which will avoid cognitive deterioration and isolation of resident users. Consequently, references will be analyzed for this emphasis, which will help us to obtain the characteristics that architecture should have within the different spaces. Finally, through the synergy of the analysis of the urban environment, the previous theory, the elaborated program, and the emphasis we provide a residential space that has a medical care area which encourages the active participation of the adult in an urban environment. at a neighborhood level, improving their quality of life. / Trabajo de investigación

Des troubles moteurs ont été décrits dans les Troubles du Spectre Autistiques (TSA), toutefois tous les enfants atteints de TSA ne montrent pas de diminution des performances motrices. La nature et l'origine des perturbations motrices dans les TSA ne sont pas claires. Les processus neuro-développementaux, en lien avec la maturation du système nerveux central, sont peu explorés dans les TSA, or ces processus sous-tendent les performances motrices. Peu d'études portent sur l'analyse fine de la sémiologie des fonctions neuro-psychomotrices dans les TSA et l'existence d'une trajectoire neuro-développementale de ces fonctions n'est pas connue chez les enfants avec TSA. L'objectif de cette étude est de mettre en évidence la sémiologie des troubles psychomoteurs auprès d'enfants avec TSA, à l'aide d'une batterie standardisée Française d'évaluation développementale des fonctions neuro-psychomotrices de l'enfant (NP-MOT) (Vaivre-Douret, 2006). L'évaluation neuro-psychomotrice complète les évaluations de premières instances (psychiatrique; psychologique; compréhension; psychomotrice). L'identification d'un profil clinique neuro-psychomoteur, l'identification de troubles ou décalages par rapport à une norme de référence, la mise en évidence de fonctions cérébrales éventuellement touchées dans les TSA devraient permettre de mieux comprendre l'origine et la nature des troubles observés dans les TSA. Les résultats de plus en plus nombreux concernant la motricité chez ces enfants doivent pouvoir également être analysés au regard des évaluations cognitives et neuro-cognitives, afin d'affiner le profil de développement et permettre ainsi de mieux comprendre la nature des troubles autistiques parmi une comorbidité d'éventuels autres dysfonctionnements. / Motor disorders have been described in the Autistic Spectrum Disorders (ASD), however all children with ASD show no decrease in motor performances. The nature and origin of motor disturbances in ASD are unclear. Neurodevelopmental processes linked to the maturation of the central nervous system, are not really explored in ASD, but these processes underlie motor performances. Few studies trat of an acute semiology of motor abnormalities in ASD and the existence of a neuro-developmental trajectory of neuro-psychomotor functions is not known in children with ASD. The aim of this study is to highlight the semiology of psychomotor disorders among children with ASD, using a French standardized neurodevelopmental assessment tool (NP-MOT) (Vaivre-Douret, 2006). Evaluations of the first instances (psychiatric; psychological; understanding; psychomotor) were supplemented by a standardized assessment battery of neuro-developmental psychomotor functions (NP-MOT). The identification of a neuro-psychomotor clinical profile, identification of problems or discrepancies compared to a standard reference, the identification of potentially affected brain functions in ASD should provide a better understanding of the origin and nature the observed disorders in ASD. The results, more and more numerous concerning motor skills in these children, should be able to be analyzed in light of cognitive or neuro-cognitive assessments and should allow to refine the profile of development and thereby enable a better understanding of the nature of autism among a comorbidity other possible malfunctions.

Introduction : L'Accident Vasculaire Cérébral (AVC) est la 1ère cause de handicap acquis chez l'adulte, dans les pays industrialisés. 20% des patients décèdent dans le mois qui suit, 75% des survivants gardent des séquelles définitives, 33% deviennent dépendant à vie. Il n'existe pour l'heure aucune thérapie de récupération quand les déficits fonctionnels sont en place hormis la rééducation. Chez l'homme, 80% des AVC thrombotiques touchent l'artère cérébrale moyenne, qui irrigue le cortex moteur primaire (M1). M1 projette des axones jusque dans la moelle épinière et forme le Faisceau Cortico Spinal (FCS). Après une atteinte de M1, ce faisceau dégénère et cela induit des déficits fonctionnels de force et de dextérité. M1 est indispensable pour les mouvements volontaires dextres garants de l'indépendance du patient. Objectif : Mise au point d'un modèle de lésion cérébrale, chez un primate non humain, le marmouset, qui permette d'évaluer la récupération fonctionnelle motrice afin d'étudier l'effet de neuro-implants. Méthode : 14 marmousets ont servi à caractériser le nouveau modèle lésionnel induit par une injection stéréotaxique d'une toxine inhibant le métabolisme cellulaire. Des tests comportementaux, évaluant le score neurologique, la dextérité et la force de traction du membre supérieur, ont permis d'évaluer la récupération fonctionnelle en phase aiguë, subaiguë et chronique jusqu'à 6 mois après la lésion. Le suivi longitudinal structural et fonctionnel de la lésion et de la récupération a été réalisé par IRM (T1, T2, DTI). Le suivi de l'intégrité du FCS a été étudié, pour la première fois chez le marmouset, grâce à une technique (ME-MRI, manganese-enhanced-MRI) utilisant un agent de contraste injecté directement dans le cortex M1, capté par les neurones et traçant les voies neuronales. Une étude pilote sur 3 marmousets a testé l'effet de neuro-implants microstructurés dans la lésion cérébrale associés à l'injection de chondroïtinase ABC (enzyme de dégradation de la matrice extracellulaire). / Introduction: Stroke is the first leading cause of acquired handicap and disability in adults in industrialized countries. 20% of patients die in the following month, 75% of survivors remain with definitive sequelae, 33% become dependent for life. No therapy in the recovery phase exists today when functional deficits are installed except rehabilitation. In human, 80% of thrombotic stroke affect middle cerebral artery, which supplies the primary motor cortex (M1). M1 projects axons to the spinal cord and forms the CorticoSpinal Tract (CST). After an M1 insult, this tract degenerates and functional deficits of force and dexterity are induced. M1 is essential for voluntary dexterous movements that make patients independent. Objective: Setting up of a cerebral lesion model in a non-human primate, the marmoset, where the functional motor recovery can be assessed in order to study thereafter the effect of neuro-implant. Methods: 14 marmosets served to characterize the new lesion model induced by stereotaxic injection of a toxin inhibiting the cellular metabolism. Behavioral tests assessing the neurological score, dexterity and pulling strength of the upper limb, could assess the functional recovery in the acute, sub-acute and chronic phases until 6 months after the lesion. The longitudinal structural and functional follow-up after the lesion and during the recovery was done with MRI (T1, T2, EPI, DTI). The follow-up of the integrity of the CST was studied for the first time in the marmoset with a technic (ME-MRI, manganese-enhanced-MRI) using a contrast agent injected directly in the cortex M1, taken up by neurons and that traced neuronal tracts. A pilot study on 3 marmosets tested the effect of micro-patterned neuro-implants in the cerebral lesion associated with the injection of chondroïtinase ABC (enzyme of extracellular matrix degradation).

[pt] Esta dissertação investiga a utilização de um sistema Neuro-Fuzzy Hierárquico para previsão de séries e a extração de regras fuzzy em aplicações de Mineração de Dados. O objetivo do trabalho foi estender o modelo Neuro- Fuzzy Hierárquico BSP para a classificação de registros e a previsão de séries temporais. O processo de classificação de registros no contexto de Mineração de Dados consiste na extração de regras de associação que melhor caracterizem, através de sua acurácia e abrangência, um determinado grupo de registros de um banco de dados (BD). A previsão de séries temporais, outra tarefa comum em Mineração de Dados tem como objetivo prever o comportamento de uma série temporal no instante t+k (k ? 1).O trabalho consistiu de 5 etapas principais: elaborar um survey dos principais sistemas e modelos mais utilizados nas aplicações de Mineração de Dados; avaliar o desempenho do sistema NFHB original em aplicações de Mineração de Dados; desenvolver uma extensão do modelo NFHB dedicado à classificação de registros em uma BD; desenvolver um novo modelo híbrido Neuro-Fuzzy Genético para o ajuste automático dos parâmetros do sistema dedicado a previsão de séries temporais; e o estudo dos casos. O estudo da área resultou num survey sobre os principais modelos para Mineração de Dados. São apresentados os modelos mais utilizados em tarefas de classificação e extração de regras tais como: redes neurais, árvores de decisão crisp e fuzzy, algoritmos genéticos, estatística e sistemas neuro-fuzzy. Na etapa de avaliação do modelo NFHB original, foi verificado que além do tradicional aprendizado dos parâmetros, comuns às redes neurais e aos sistemas neuro-fuzzy, o modelo possui as seguintes aracterísticas: aprendizado da estrutura, a partir do uso de particionamentos recursivos; número maior de entradas que o habitualmente encontrado nos sistemas neuro-fuzzy; e regras com hierarquia, características adequadas para as aplicações de Mineração de Dados. Entretanto, o processo de extração de regras e a seleção de atributos não são adequados para este tipo de aplicação, assim como a excessiva complexidade da parametrização do modelo para aplicações de previsão de séries temporais. Uma extensão ao modelo NFHB original foi então proposta para aplicações de classificação de registros no contexto da Mineração de Dados onde se têm como objetivo principal a extração de informação em forma de regras interpretáveis. Foi necessário modificar a seleção de atributos e o processo original de extração de regras. O sistema fuzzy do tipo Takagi-Sugeno do modelo NFHB original fornece regras inadequadas do ponto de vista da Mineração de Dados. O novo modelo NFHB, dotado das modificações necessárias, mostrou um ótimo desempenho na extração de regras fuzzy válidas que descrevem a informação contida no banco de dados. As medidas de avaliação normalmente usadas para analisar regras crisp (Se x1 é <14.3 e...), como abrangência e acurácia, foram modificadas para poderem ser aplicadas ao caso de avaliação das regras fuzzy (Se x1 é Baixo e..) extraídas pelo sistema NFHB após da fase de aprendizado. A quantidade e a qualidade das regras extraídas é um ponto fundamental dos sistemas voltados para aplicações de Mineração de Dados, que buscam sempre obter o menor número de regras e da maior qualidade possível. Nesse sentido, o processo de seleção das características de entrada foi alterado para evitar particionamentos excessivos, ou seja regras desnecessárias. Foram implementadas duas estratégias de seleção (Fixa e Adaptativa) em função de diferentes medidas de avaliação como a Entropia e o método de Jang. Um novo modelo híbrido neuro-fuzzy genético para previsão de séries temporais foi criado para resolver o problema da excessiva complexidade de parametrização do sistema, o qual conta com mais de 15 parâmetros.Foi proposto um novo modelo híbrido neuro-fuzzy genético capaz de evoluir e obter um conjunto de parâmetros adequado par / [en] This dissertation investigates the use of a Neuro-Fuzzy Hierarchical system for time series forecasting and fuzzy rule extraction for Data Mining applications. The objective of this work was to extend the Neuro-Fuzzy BSP Hierarchical model for the classification of registers and time series forecasting. The process of classification of registers in the Data Mining context consists of extracting association rules that best characterise, through its accuracy and coverage measures, a certain group of registers of database (DB). The time series forecasting other common task in Data Mining, has a main objective to foresee the behavior of a time series in the instant t+k (k>=1). The work consisted of 5 main stages: to elaborate a survey of the main systems and the most common models in Data Mining applications; to evaluate the performance of the original NFHB system in Data Mining applicatons; to develop an extension of the NFHB model dedicated to the classification of registers in a DB; to develop a new Neuro-Fuzzy Genetic hybrid model for the automatic adjustment of the parameters of the system for time series forecasting applicatons; and the case estudies. The study of the area resulted in a survey of the main Data Mining models. The most common methods used in Data Mining application are presented such as: neural nets, crisp and fuzzy decision trees, genetic algorithms, statistics and neuro-fuzzy systems. In the stage of evaluation of the original NFHB model, it verified that besides the traditional learning of the parameters, common to the neural nets and the neuro-fuzzy systems, the model possesses the following characteristics: learning of the structure; recursive partitioning; larger number of inputs than usually found on the neuro-fuzzy systems; rule with hierarchy; which are characteristics adapted for Data Mining applications. However the rule extraction process and attributes selection are not appropriate for this type of applications, as well as the excessive complexity of the tuning of the model for time series forecasting applicatons. An extension of the original NFHB model was then proposed for applicatons of classification of registers in the Data Mining context, where the main objective in the extraction of information in form of interpratable rules. It was necessary to modify the attributes selection and the original rule extraction process. The Takagi-Sugeno fuzzy system of the original NFHB model supplies inadequate rules, from the Data Mining point of view. The new NFHB models, endowed with necessary modifications, showed good performance in extracting valid fuzzy rules that describe the information contained in the database. The evaluation metrics, usually used to analyse crips rules (If x1 is <14.3 and), as coverage and accuracy, were modified to be applied to the evaluation of the fuzzy rules (If x1 is Low and) extracted from the NFHB system after the learning process. The amount and quality of the extracted rules are important points of the systems dedicated for Data Mining applicatons, where the target is to obtain the smallest number of rules and of the best quality. In that sense, the input selection strategies were implemented (Static and Adaptive), using different evaluation measures as Entropy and the jang algorithm. A new genetic neuro-fuzzy hybrid model for time series forecasting was created to solve the problem of the excessive complexity of the model tuning, which comprises more than 15 parameters. A new model wes proposed, a genetic neuro-fuzzy hybrid, model capable to develop and to obtain an appropriate set of parameters for the forecasting of time series. The new hybrid, model capable to develop and to obtain an appropriate set of parameters for the forecasting of time series. The new hybrid model presented good results with different types of series. A tool based on the NFHB model was developed for classification and forecasting applications. Th

[pt] O envelhecimento da voz, conhecido como presbifonia, é um processo natural que pode causar grande modificação na qualidade vocal do indivíduo. A sua identificação precoce pode trazer benefícios, buscando tratamentos que possam prevenir o seu avanço. Esse trabalho tem como motivação a identificação de vozes com sinais de envelhecimento através de redes neurais do tipo Spiking (SNN). O objetivo principal é o de construir dois novos modelos, denominados híbridos, utilizando SNN para problemas de agrupamento, onde os atributos de entrada e os parâmetros que configuram a SNN são otimizados por algoritmos evolutivos. Mais especificamente, os modelos neuro-evolucionários propostos são utilizados com o propósito de configurar corretamente a SNN, e selecionar os atributos mais relevantes para a formação dos grupos. Os algoritmos evolutivos utilizados foram o Algoritmo Evolutivo com Inspiração Quântica com representação Binário-Real (AEIQ-BR) e o Optimization by Genetic Programming (OGP). Os modelos resultantes foram nomeados Quantum-Inspired Evolution of Spiking Neural Networks with Binary-Real (QbrSNN) e Spiking Neural Network Optimization by Genetic Programming (SNN-OGP). Foram utilizadas oito bases benchmark e duas bases de voz, masculinas e femininas, a fim de caracterizar o envelhecimento. Para uma análise funcional da SNN, as bases benchmark forma testadas com uma abordagem clássica de agrupamento (kmeans) e com uma SNN sem evolução. Os modelos propostos foram comparados com uma abordagem clássica de Algoritmo Genético (AG). Os resultados mostraram a viabilidade do uso das SNNs para agrupamento de vozes envelhecidas. / [en] The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. Its early identification can benefit, seeking treatments that could prevent their advance. This work is motivated by the identification of voices with signs of aging through neural networks of spiking type (SNN). The main objective is to build two new models, called hybrids, using SNN for clustering problems where the input attributes and parameters that configure the SNN are optimized by evolutionary algorithms. More specifically, the proposed neuro-evolutionary models are used in order to properly configure the SNN, and select the most relevant attributes for the formation of groups. Evolutionary algorithms used were the Evolutionary Algorithm with Quantum Inspiration with representation Binary-Real (AEIQ-BR) and the Optimization by Genetic Programming (OGP). The resulting models were named Quantum-Inspired Spiking Neural Evolution of Networks with Binary-Real (QbrSNN) and Spiking Neural Network Optimization by Genetic Programming (SNN-OGP). Eight bases were used, and two voice benchmark bases, male and female, in order to characterize aging. NNS for functional analysis, the tested benchmark base form with a classical clustering approach (kmeans) and a SNN without change. The proposed models were compared with a classical approach of Genetic Algorithm (GA). The results showed the feasibility of using the SNN to agrupamentode aged voices.